DEVELOPMENT OF AN INDIVIDUAL-LEVEL SIMULATION MODEL TO ASSESS THE COST CONSEQUENCES OF CARDIOVASCULAR-RENAL-METABOLIC SCREENING POLICIES FROM A NATIONAL PAYER AND SOCIETAL PERSPECTIVE
Author(s)
Georgios Xydopoulos, PhD1, Fiona Adshead, MSc2, Alexandre Babin, MSc3, Amitava Banerjee, MA, MPH, DPhil, PGCert4, Bruno DETOURNAY, MA, MBA, MSc, MD5, Jaime Espin, MSc, PhD6, Ines Hassan7, Matt Kearney, MPH, MD8, Jan T. Kielstein, MD9, Koichiro Kuwahara, MD, PhD10, Carel le Roux, MD PhD11, Nirosha Lederer, PhD12, Lise RETAT, PhD13, Scott B. Robinson, MA, MPH14, Vasileios Vasilopoulos, MSc15, Juergen Wasem, PhD16, Clélia-Elsa Froguel , BA Pgd17;
1ZS Associates, Cambridge, United Kingdom, 2Sustainable Healthcare Coalition, London, United Kingdom, 3Renaloo, Paris, France, 4University College London Hospitals, London, United Kingdom, 5CEMKA, Antony, France, 6EASP, Granada, Spain, 7London, United Kingdom, 8CVD Action, London, United Kingdom, 9Universität Braunschweig, Braunschweig, Germany, 10Shinshu University School of Medicine, Matsumoto, Japan, 11University College Dublin, Dublin, Ireland, 12AstraZeneca, Washington, DC, USA, 13AstraZeneca, Barcelona, Spain, 14ZS Associates, Durham, NC, USA, 15ZS Associates, London, United Kingdom, 16University Duisburg, Essen, Germany, 17AstraZeneca, Cambridge, United Kingdom
1ZS Associates, Cambridge, United Kingdom, 2Sustainable Healthcare Coalition, London, United Kingdom, 3Renaloo, Paris, France, 4University College London Hospitals, London, United Kingdom, 5CEMKA, Antony, France, 6EASP, Granada, Spain, 7London, United Kingdom, 8CVD Action, London, United Kingdom, 9Universität Braunschweig, Braunschweig, Germany, 10Shinshu University School of Medicine, Matsumoto, Japan, 11University College Dublin, Dublin, Ireland, 12AstraZeneca, Washington, DC, USA, 13AstraZeneca, Barcelona, Spain, 14ZS Associates, Durham, NC, USA, 15ZS Associates, London, United Kingdom, 16University Duisburg, Essen, Germany, 17AstraZeneca, Cambridge, United Kingdom
OBJECTIVES: Cardiovascular, renal, and metabolic (CVRM) diseases are interconnected conditions that collectively impose a major burden on health systems. Despite clinical interdependence, current screening policies are often developed in isolation, potentially limiting overall effectiveness. This study aimed to develop an individual-level microsimulation model to estimate the cost-consequence of integrated CVRM screening policies from both national payer and societal perspectives.
METHODS: To support policy recommendations, we developed a discrete-time, patient-level microsimulation model to simulate disease onset, progression, and outcomes across six interrelated CVRM conditions: heart failure, hypertension, obesity, dyslipidemia, chronic kidney disease, and type 2 diabetes. Model outcomes included clinical trajectories, resource utilization, productivity losses and environmental impact (CO2e emissions).Model development and key assumptions were guided by an international Steering Committee, comprised of clinicians, economists and policy experts, with input refined through interim reviews. Dynamic risk equations, multimorbidity interactions, and annual health-state transitions were incorporated within a closed cohort of nationally representative synthetic populations from Australia, France, Germany, United Kingdom. Targeted literature reviews informed key clinical, epidemiological, and economic parameters, as well as the selection of policy-relevant inputs and outcomes.
RESULTS: The model simulated disease trajectories across six CVRM conditions, estimating annual and lifetime health outcomes, healthcare utilization, and costs under alternative screening scenarios. Comparative analyses indicated that integrated CVRM screening strategies were associated with improved health outcomes and potential long-term cost savings relative to single-condition screening approaches. Results also supported the assessment of return on investment and broader societal impacts across national health system contexts.
CONCLUSIONS: This model provides a robust framework for evaluating combined CVRM screening policies and their economic implications. By integrating expert input and literature-based evidence, it can inform national screening strategy design, support resource allocation decisions, and enhance population health outcomes. Future applications include scenario testing for emerging screening technologies and evolving policy priorities.
METHODS: To support policy recommendations, we developed a discrete-time, patient-level microsimulation model to simulate disease onset, progression, and outcomes across six interrelated CVRM conditions: heart failure, hypertension, obesity, dyslipidemia, chronic kidney disease, and type 2 diabetes. Model outcomes included clinical trajectories, resource utilization, productivity losses and environmental impact (CO2e emissions).Model development and key assumptions were guided by an international Steering Committee, comprised of clinicians, economists and policy experts, with input refined through interim reviews. Dynamic risk equations, multimorbidity interactions, and annual health-state transitions were incorporated within a closed cohort of nationally representative synthetic populations from Australia, France, Germany, United Kingdom. Targeted literature reviews informed key clinical, epidemiological, and economic parameters, as well as the selection of policy-relevant inputs and outcomes.
RESULTS: The model simulated disease trajectories across six CVRM conditions, estimating annual and lifetime health outcomes, healthcare utilization, and costs under alternative screening scenarios. Comparative analyses indicated that integrated CVRM screening strategies were associated with improved health outcomes and potential long-term cost savings relative to single-condition screening approaches. Results also supported the assessment of return on investment and broader societal impacts across national health system contexts.
CONCLUSIONS: This model provides a robust framework for evaluating combined CVRM screening policies and their economic implications. By integrating expert input and literature-based evidence, it can inform national screening strategy design, support resource allocation decisions, and enhance population health outcomes. Future applications include scenario testing for emerging screening technologies and evolving policy priorities.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
MSR152
Topic
Methodological & Statistical Research
Disease
SDC: Cardiovascular Disorders (including MI, Stroke, Circulatory), SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)